This paper proposes a novel routing metrics based on the residual bandwidth, energy and mobility index of the nodes. Metrics are designed to cope with high mobility and poor residual energy resources in order to find optimal paths that guarantee the QoS constraints. A maximizable routing metric theory has been used to develop a metric that selects, during the protocol process, routes that are more stable, offer a maximum throughput and prolong network life time. The OLSR (Optimized Link State Routing) protocol, which is an optimization of link state protocols designed for MANETs (Mobile Ad hoc Networks) is used as a test bed in this work. We prove that our proposed composite metrics (based on mobility, energy and bandwidth) selects a more stable MPR set than the QOLSR algorithm which is a well known OLSR QoS extension. By mathematical analysis and simulations, we have shown the efficiency of this new routing metric in term of routing load, packet delivery fraction, delay and prolonging the network lifetime.

This paper proposes a novel routing metrics based on the residual bandwidth, energy and mobility index of the nodes. Metrics are designed to cope with high mobility and poor residual energy resources in order to find optimal paths that guarantee the QoS constraints. A maximizable routing metric theory has been used to develop a metric that selects, during the protocol process, routes that are more stable, offer a maximum throughput and prolong network life time. The OLSR (Optimized Link State Routing) protocol, which is an optimization of link state protocols designed for MANETs (Mobile Ad hoc Networks) is used as a test bed in this work. We prove that our proposed composite metrics (based on mobility, energy and bandwidth) selects a more stable MPR set than the QOLSR algorithm which is a well known OLSR QoS extension. By mathematical analysis and simulations, we have shown the efficiency of this new routing metric in term of routing load, packet delivery fraction, delay and prolonging the network lifetime.

This paper proposes a novel routing metrics based onthe residual bandwidth, energy and mobility index of the nodes.Metrics are designed to cope with high mobility and poorresidual energy resources in order to find optimal paths thatguarantee the QoS constraints. A maximizable routing metrictheory has been used to develop a metric that selects, during theprotocol process, routes that are more stable, offer a maximumthroughput and prolong network life time. The OLSR(Optimized Link State Routing) protocol, which is anoptimization of link state protocols designed for MANETs(Mobile Ad hoc Networks) is used as a test bed in this work. Weprove that our proposed composite metrics (based on mobility,energy and bandwidth) selects a more stable MPR set than theQOLSR algorithm which is a well known OLSR QoS extension.By mathematical analysis and simulations, we have shown theefficiency of this new routing metric in term of routing load,packet delivery fraction, delay and prolonging the networklifetime.

A Mobile Ad hoc Network (MANET) is a collection of mobile nodes working on a dynamic autonomous network.Nodes communicate with each other over the wireless mediumwithout need of a centralized access points or a base station.Since there is no existing communication infrastructure,adhoc networks cannot rely on specialised routers for pathdiscovery and routing. Therefore, nodes in such a network areexpected to act cooperatively to establish routes instantly.Such a network is also expected to route traffic, possibly overmultiple hops, in distributed manner, and to adapt itself to thehighly dynamic changes of its links , mobility and residualenergy patterns of its constituent nodes.Providing QoS in MANETs [1] is a tedious task. It’s knownthat combining multiple criteria in the routing process is aHard problem (NP-Complet) A complete QoS model inMANETs will span multiple layers, however the network layer plays a vital role in providing the required supportmechanisms. The goal of QoS routing is to obtain feasiblepaths that satisfy end-system performance requirements. MostQoS routing algorithms present an extension of existingclassic best effort routing algorithms. There are three maincategoriesof MANET routing protocols: Proactive (table-driven),Reactive (on-demand) and Hybrid. Proactive protocols buildtheir routing tables continuously by broadcasting periodicrouting updates through the network; reactive protocols buildtheir routing tables on demand and have no prior knowledgeof the route they will take to get to a particular node. Hybridprotocols create reactive routing zones interconnected byproactive routing links and usually adapt their routing strategyto the amount of mobility in the network.In this paper we reiterate our proposed mobility metric.Based on the use of this mobility metric we propose a newcomposite metric, to find the optimal path given the QoSconstraints. The objective of the composite metric is to find anoptimal stable path with maximum available bandwidth and toprolong network life time.Using the OLSR Protocol, we show that our proposedmetric selects stable MPR Set rather than the QOLSRalgorithm which is a well known OLSR QoS algorithm forMANETs.This paper is organized as follows. Section 2 gives anoverview of the original OLSR protocol. Section 3 summarizesthe state of the art dealing with QoS support in MANETs anddescribes the QoS routing problems Section 4 presents ourproposed composite metric based on mobility, residual energyand bandwidth as QoS parameters. In Section 5, simulationsand results are discussed. The last part of this paper concludesand presents some future work.II.

O

PTIMIZED

L

INK

S

TATE

R

OUTING

P

ROTOCOL

A.

Overview

OLSR (Optimized Link State Routing) protocol [2-3] is aproactive table driven routing protocol for mobile ad hocnetworks and it is fully described on RFC 3626 (ThomasClausen & Philippe Jacquet, (October 2003)). As a link staterouting protocol, OLSR periodically advertises the linksbuilding the network. However, OLSR optimizes the topologyinformation flooding mechanism, by reducing the amount of links that are advertised and by reducing the number of nodesforwarding each topology message to the set of

and exchanged using broadcasted into the network. TCmessages are only originated by nodes selected as

Multipoint Relays (MPRs)

by some other node in the network.

MPRs

areselected in such a way that a minimum amount of

MPRs

,located one-hop away from the node doing the selection(called

MPR Selector

), are enough to reach every singleneighbour located two-hops away of

MPR selector

. Byapplying this selection mechanism only a reduced amount of nodes (depending on the network topology) will be selected asMPRs[18]. Every node in the network is aware of its one-hopand two-hop neighbours by periodically exchanging

HELLO

messages containing the list of its one-hop neighbours. On theother hand, TC messages will only advertise the links betweenthe MPRs and their electors. Then, only a partial amount of the network links (the topology) will be advertised, also MPRsare the only nodes allowed to forward TC messages and onlyif messages come from a

MPR Selector

node. Theseforwarding constrains considerably decrease the amount of flooding retransmissions (Figure 1). This example shows theefficiency of the MPR mechanism because only eighttransmissions are required to reach all the 23 nodes buildingthe network, which is a significant saving when compared totraditional flooding mechanism where every node is asked toretransmit to all neighbours.Figure 1: Flooding with MPR mechanism

B.

MPR Selection Algorithm

The computation of the

MPR set

with minimal size is a NP-complet problem [14-16]. For this end, the standard MPRselection algorithm currently used in the OLSR protocolimplementations is as follows:Figure 2-a- Example of MRRset calculation.For a node

uses hop count to compute the shortest path to anarbitrary destination using the topology map consisting of allits neighbours and of

MPRs

of all other nodes. Number of hopcriterion as a routing metric is not suitable for QoS support asa path selected based on the least number of hops may notsatisfy the required QoS constraints.Figure 2-b: MPR Selection AlgorithmIII.

R

ELATED WORK

A.

Qos Support in a Manet

In this section we discuss the recent work done to provide

QoS functionality

in Manets.INSIGNIA, [7], is an adaptation of the IntServ Model to themobile ad hoc networks.

QoS

guarantee is done by per-flowinformation in each node that is set up by thesignalling/reservation protocol. The destination statisticallymeasures

to thesource. Based on those reports, the source node can adapt real-time flows to avoid congestion.SWAN, [13], Service differentiation in stateless WirelessAd-hoc Network, is an adaptation of the DiffServ Model to themobile ad-hoc networks. Nodes do not need to keep per-flowinformation in order to handle packets.

QoS guarantee

isprovided according to the class of the flow once it has beenaccepted.FQMM, [11], Flexible

Qos Model

for MANET, has beenintroduced to offer a better QoS guarantee to a restrictednumber of flows whereas a class guarantee is offered to theother flows. FQMM is a hybrid approach combining per-flowgranularity of

are based on considering only bandwidth as a QoS routingconstraint and revisions to the MPR selection criteria. Theyidentify that MPR selection is vital in optimal path selection.The key concept in the revised MPR selection algorithm isthat a “good bandwidth” link should never be omitted. Basedon this three algorithms were proposed: OLSR_R1, R2 andR1.The research group at INRIA [9],[10] proposed a QoSrouting scheme over OLSR. Their technique used delay andbandwidth metric for routing table computation. Such metricsare included on each routing table entry corresponding to eachdestination.

QOLSR

[11] and work presented in [9] enhance OLSR withQoS support. Both propose a solution providing a path suchthat the bandwidth available at each node on the path ishigher than or equal to the requested bandwidth. Furthermore,

QOLSR

considers delay as a second criterion for pathselection.However, all of these solutions do not take into account atall mobility and energy parameters induced by the nature of Manet Network.

B.

Qos Routing Problems

One of the key issues in providing

end-to-end QoS

in agiven network is how to find a feasible path that satisfies the

QoS constraints

. The problem of finding a feasible path is

NP-Complete

if the number of constraints is more than two, itcannot be exactly solved in polynomial time and mostly dealtwith using heuristics and approximations. The network layerhas a critical role to play in the QoS provision process. Theapproaches used by the QoS routing algorithms follow a trade-off between the optimality of paths and the complexity of algorithms especially in computing multiconstrained path. Asurvey on such solutions can be found in [14].The computation complexity is primarily determined by thecomposition rules of the metrics [16]. The three basiccomposition rules are: additive (such as delay, delay jitter,logarithm of successful transmission, hop count and cost),multiplicative (like reliability and probability of successfultransmission) and concave/min-max (e.g. bandwidth). Theadditive and multiplicative metric of a path is the sum andmultiplication of the metric respectively for all the linksconstituting the path. The concave metric of a path is themaximum or the minimum of the metric over all the links inthe path.Otherwise, if

ji

M

;

is the metric for link

{ }

j,

i

and

P

is thepath between

(i, j, k,..1,m)

nodes, the QoS metric

M(P)

isdefined as [14-15]:

•

Additive

: M(P) =

ji

M

;

+

k i

M

;

+…+

ml

M

;

•

Multiplicative

: M(P) =

ji

M

;

x

k i

M

;

x…x

ml

M

;

•

Concave

: M(P) =

{ }

M,...,M,Mmin

ml;k i; ji;

The proof of

NP-Completeness

relies heavily on thecorrelation of the link weight metrics. QoS Routing is NP-Complete when the QoS metrics are independent, realnumbers or unbounded integers.In general, QoS routing focuses on how to find feasible andoptimal paths that satisfy QoS requirements of various voice,video and data applications. However, based on

maximizablerouting metrics theory

[16], it is shown that two or morerouting metrics can be combined to form a composite metric if the original metrics are bounded and monotonic.Before we proceed to the mathematical proof, we givedefinitions of maximal metric tree and the properties desiredfor combining metrics i.e.

bounded- ness and monotonicity.

Definition 1:

Routing Metric

A routing metric for a network N is

six-tuple

(W,Wf, M, mr, met,

R RR R

) where:

1.

M is a set of metric values2.

Wf is a function that assigns to each edge

{ }

j,

i

in N aweight Wf(

{ }

j,

i

) in W3.

W is a set of edge weights4.

mr is a metric value in M assigned to the root.5.

met is a metric function whose domain is MxW andwhose range is M (it takes a metric value and an edgevalue and returns a metric value).6.

R RR R

is a binary relation over m, the set of metric values thatsatisfy the following four conditions of irreflexivity,

Definition 2

:

Maximum Metric Tree

A spanning tree of N is called a maximum metric tree withrespect to an assigned metric iff every rooted path in T ismaximum metric with respect to the assigned metric. Insimple words every node obtains its maximum metric throughits path along a maximum metric tree.